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    "<h1> PTRAIL Interpolation </h1>\n",
    "<h2> Random Walk and Kinematic Interpolation</h2>\n",
    "<p>\n",
    "    This Notebook contains the examples for Radnom Walk and Kinematic Interpolations.\n",
    "    It reads a file and interpolates points first using Random Walk and then Kinematic\n",
    "    interpolation method by providing the dataframe and a time difference\n",
    "    threshold beyond which points need to be interpolated and the interpolation\n",
    "    type which by default is linear. The original data, and interpolated datas are\n",
    "    all plotted to show the difference between trajectories. Segments of the original datas\n",
    "    are taken and interpolated, and by plotting it a clear difference in the interpolation points are shown.\n",
    "</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "The following datasets have been :\n",
    "<ul>\n",
    "   <li> <a href=\"https://github.com/YakshHaranwala/PTRAIL/blob/main/examples/data/gulls.csv\" target=\"_blank\"> Seagulls Dataset </a> </li>\n",
    "   <li> <a href=\"https://github.com/YakshHaranwala/PTRAIL/blob/main/examples/data/atlantic.csv%22%3E\"> Hurricane Dataset </a> </li>\n",
    "</ul>\n",
    "\n",
    "<hr>\n",
    "<p align='justify'>\n",
    "Note: Viewing this notebook in GitHub will not render JavaScript\n",
    "elements. Hence, for a better experience, click the link below\n",
    "to open the Jupyter notebook in NB viewer.\n",
    "\n",
    "<span> &#8618; </span>\n",
    "<a href=\"https://nbviewer.jupyter.org/github/YakshHaranwala/PTRAIL/blob/main/examples/5.%20RandomWalk_And_Kinematic_IP.ipynb\" target='_blank'> Click Here </a>\n",
    "</p>"
   ],
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    "pycharm": {
     "name": "#%% md\n"
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "from ptrail.core.TrajectoryDF import PTRAILDataFrame\n",
    "from ptrail.preprocessing.interpolation import Interpolation as ip\n",
    "from ptrail.utilities.conversions import Conversions as con\n",
    "import folium\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import ptrail.utilities.constants as const\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 283 ms, sys: 12.1 ms, total: 295 ms\n",
      "Wall time: 294 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "                               event-id  visible       lon       lat  \\\ntraj_id DateTime                                                       \n91732   2009-05-27 14:00:00  1082620685     True  24.58617  61.24783   \n        2009-05-27 20:00:00  1082620686     True  24.58217  61.23267   \n        2009-05-28 05:00:00  1082620687     True  24.53133  61.18833   \n        2009-05-28 08:00:00  1082620688     True  24.58200  61.23283   \n        2009-05-28 14:00:00  1082620689     True  24.58250  61.23267   \n\n                            sensor-type individual-taxon-canonical-name  \\\ntraj_id DateTime                                                          \n91732   2009-05-27 14:00:00         gps                    Larus fuscus   \n        2009-05-27 20:00:00         gps                    Larus fuscus   \n        2009-05-28 05:00:00         gps                    Larus fuscus   \n        2009-05-28 08:00:00         gps                    Larus fuscus   \n        2009-05-28 14:00:00         gps                    Larus fuscus   \n\n                            individual-local-identifier  \\\ntraj_id DateTime                                          \n91732   2009-05-27 14:00:00                      91732A   \n        2009-05-27 20:00:00                      91732A   \n        2009-05-28 05:00:00                      91732A   \n        2009-05-28 08:00:00                      91732A   \n        2009-05-28 14:00:00                      91732A   \n\n                                                                    study-name  \ntraj_id DateTime                                                                \n91732   2009-05-27 14:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-27 20:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 05:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 08:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 14:00:00  Navigation experiments in lesser black-backed ...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>event-id</th>\n      <th>visible</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>sensor-type</th>\n      <th>individual-taxon-canonical-name</th>\n      <th>individual-local-identifier</th>\n      <th>study-name</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">91732</th>\n      <th>2009-05-27 14:00:00</th>\n      <td>1082620685</td>\n      <td>True</td>\n      <td>24.58617</td>\n      <td>61.24783</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-27 20:00:00</th>\n      <td>1082620686</td>\n      <td>True</td>\n      <td>24.58217</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 05:00:00</th>\n      <td>1082620687</td>\n      <td>True</td>\n      <td>24.53133</td>\n      <td>61.18833</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 08:00:00</th>\n      <td>1082620688</td>\n      <td>True</td>\n      <td>24.58200</td>\n      <td>61.23283</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 14:00:00</th>\n      <td>1082620689</td>\n      <td>True</td>\n      <td>24.58250</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Reading the gulls dataset and converting to PTRAILDataFrame.\n",
    "# Also, lets, print the first 5 points of the dataset to\n",
    "# see how the dataframe looks.\n",
    "gulls = pd.read_csv('./data/gulls.csv')\n",
    "np_gulls = PTRAILDataFrame(gulls,\n",
    "                           latitude='location-lat',\n",
    "                           longitude='location-long',\n",
    "                           datetime='timestamp',\n",
    "                           traj_id='tag-local-identifier',\n",
    "                           rest_of_columns=[])\n",
    "np_gulls.head()"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     1501\n",
      "False     469\n",
      "Name: DateTime, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Here, we will filter out a single trajectory from the atlantic\n",
    "# dataset and check how many of the trajectory's points have\n",
    "# time jump greater than what the examples are using to interpolate\n",
    "\n",
    "small_gulls = np_gulls.reset_index().loc[np_gulls.reset_index()[const.TRAJECTORY_ID] == '91732'][[const.TRAJECTORY_ID, const.DateTime, const.LAT, const.LONG]]\n",
    "time_del = small_gulls.reset_index()[const.DateTime].diff().dt.total_seconds()\n",
    "print((time_del > 3600*4).value_counts())"
   ],
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     "name": "#%%\n"
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      "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, we plot the smaller trajectory on a folium map.\n",
    "sw = small_gulls[['lat', 'lon']].min().values.tolist()\n",
    "ne = small_gulls[['lat', 'lon']].max().values.tolist()\n",
    "coords = [zip(small_gulls[const.LAT], small_gulls[const.LONG])]\n",
    "m1 = folium.Map()\n",
    "\n",
    "folium.PolyLine(coords,\n",
    "                color='blue',\n",
    "                weight=2,\n",
    "                opacity=0.7).add_to(m1)\n",
    "m1.fit_bounds([sw, ne])\n",
    "display(m1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Length: 1970\n",
      "Interpolated Length: 3471\n",
      "CPU times: user 16 ms, sys: 15.9 ms, total: 31.9 ms\n",
      "Wall time: 4.03 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using random-walk interpolation.\n",
    "\n",
    "small_rw_gulls = ip.interpolate_position(small_gulls,\n",
    "                                          3600*4,\n",
    "                                          ip_type='random-walk')\n",
    "print(f\"Original Length: {len(small_gulls)}\")\n",
    "print(f\"Interpolated Length: {len(small_rw_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Length: 1970\n",
      "Interpolated Length: 3470\n",
      "CPU times: user 15.1 ms, sys: 23.7 ms, total: 38.8 ms\n",
      "Wall time: 4.37 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using kinematic interpolation.\n",
    "\n",
    "small_kin_gulls = ip.interpolate_position(small_gulls,\n",
    "                                          3600*4,\n",
    "                                          ip_type='kinematic')\n",
    "print(f\"Original Length: {len(small_gulls)}\")\n",
    "print(f\"Interpolated Length: {len(small_kin_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1800 with 3 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, plot the scatter points of following 3 trajectories:\n",
    "# 1. Original Small DF.\n",
    "# 2. Linear-Interpolated Small DF.\n",
    "# 3. Cubic-Interpolated Small DF.\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(25, 25))\n",
    "axes[0].scatter(small_gulls[const.LAT],\n",
    "                small_gulls[const.LONG],\n",
    "                s=100, color='brown')\n",
    "axes[0].set_title('Original', fontsize=60, color='grey')\n",
    "axes[1].scatter(small_rw_gulls[const.LAT],\n",
    "                small_rw_gulls[const.LONG],\n",
    "                s=100, color='blue')\n",
    "axes[1].set_title('Random-Walk', fontsize=60, color='grey')\n",
    "axes[2].scatter(small_kin_gulls[const.LAT],\n",
    "                small_kin_gulls[const.LONG],\n",
    "                s=100, color='green')\n",
    "axes[2].set_title('Kinematic', fontsize=60, color='grey')\n",
    "\n",
    "fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    1. Reading the atlantic dataset, cleaning it up and then\n",
    "       converting it to PTRAILDataFrame.\n",
    "    2. It is to be noted that apart from reading the dataset,\n",
    "       before converting to PTRAILDataFrame, the dataframe needs\n",
    "       some cleanup as the Time format provided in the dataframe\n",
    "       needs to be first converted into a library supported time\n",
    "       format. Also, the format of the coordinates need to be\n",
    "       converted to library supported format before converting'\n",
    "       it to PTRAILDataFrame.\n",
    "    3. Also, lets, print the first 5 points of the dataset to\n",
    "      see how the dataframe looks.\n",
    "\"\"\"\n",
    "atlantic = pd.read_csv('./data/atlantic.csv')\n",
    "atlantic = con.convert_directions_to_degree_lat_lon(atlantic, 'Latitude',\"Longitude\")\n",
    "def convert_to_datetime(row):\n",
    "        this_date = '{}-{}-{}'.format(str(row['Date'])[0:4], str(row['Date'])[4:6], str(row['Date'])[6:])\n",
    "        this_time = '{:02d}:{:02d}:00'.format(int(row['Time']/100), int(str(row['Time'])[-2:]))\n",
    "        return '{} {}'.format(this_date, this_time)\n",
    "atlantic['DateTime'] = atlantic.apply(convert_to_datetime, axis=1)\n",
    "np_atlantic = PTRAILDataFrame(atlantic,\n",
    "                              latitude='Latitude',\n",
    "                              longitude='Longitude',\n",
    "                              datetime='DateTime',\n",
    "                              traj_id='ID')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     1501\n",
      "False     469\n",
      "Name: DateTime, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Here, we will filter out a single trajectory from the atlantic\n",
    "# dataset and check how many of the trajectory's points have\n",
    "# time jump greater than what the examples are using to interpolate\n",
    "\n",
    "small_atlantic = np_atlantic.reset_index().loc[np_atlantic.reset_index()[const.TRAJECTORY_ID] == 'AL062010'][[const.TRAJECTORY_ID, const.DateTime, const.LAT, const.LONG]]\n",
    "time_del = small_gulls.reset_index()[const.DateTime].diff().dt.total_seconds()\n",
    "print((time_del > 3600*4).value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<folium.folium.Map at 0x7f057e41f9d0>",
      "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, we plot the smaller trajectory on a folium map.\n",
    "sw = small_atlantic[['lat', 'lon']].min().values.tolist()\n",
    "ne = small_atlantic[['lat', 'lon']].max().values.tolist()\n",
    "coords = [zip(small_atlantic[const.LAT], small_atlantic[const.LONG])]\n",
    "m2 = folium.Map()\n",
    "\n",
    "folium.PolyLine(coords,\n",
    "                color='blue',\n",
    "                weight=2,\n",
    "                opacity=0.7).add_to(m2)\n",
    "m2.fit_bounds([sw, ne])\n",
    "display(m2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Length: 51\n",
      "Interpolated Length: 101\n",
      "CPU times: user 15.9 ms, sys: 11.9 ms, total: 27.8 ms\n",
      "Wall time: 270 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using random-walk interpolation.\n",
    "\n",
    "small_rw_atlantic = ip.interpolate_position(small_atlantic,\n",
    "                                            3600*4,\n",
    "                                            ip_type='random-walk')\n",
    "print(f\"Original Length: {len(small_atlantic)}\")\n",
    "print(f\"Interpolated Length: {len(small_rw_atlantic)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Length: 51\n",
      "Interpolated Length: 100\n",
      "CPU times: user 19 ms, sys: 7.98 ms, total: 26.9 ms\n",
      "Wall time: 262 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using kinematic interpolation.\n",
    "\n",
    "small_kin_atlantic = ip.interpolate_position(small_atlantic,\n",
    "                                          3600*4,\n",
    "                                          ip_type='kinematic')\n",
    "print(f\"Original Length: {len(small_atlantic)}\")\n",
    "print(f\"Interpolated Length: {len(small_kin_atlantic)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1800 with 3 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, plot the scatter points of following 3 trajectories:\n",
    "# 1. Original Small DF.\n",
    "# 2. Linear-Interpolated Small DF.\n",
    "# 3. Cubic-Interpolated Small DF.\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(25, 25))\n",
    "axes[0].scatter(small_atlantic[const.LAT],\n",
    "                small_atlantic[const.LONG],\n",
    "                s=100, color='brown')\n",
    "axes[0].set_title('Original', fontsize=60, color='grey')\n",
    "axes[1].scatter(small_rw_atlantic[const.LAT],\n",
    "                small_rw_atlantic[const.LONG],\n",
    "                s=100, color='blue')\n",
    "axes[1].set_title('Random-Walk', fontsize=60, color='grey')\n",
    "axes[2].scatter(small_kin_atlantic[const.LAT],\n",
    "                small_kin_atlantic[const.LONG],\n",
    "                s=100, color='green')\n",
    "axes[2].set_title('Kinematic', fontsize=60, color='grey')\n",
    "\n",
    "fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF length: 89869\n",
      "Random-Walk Interpolated DF length: 157775\n",
      "CPU times: user 208 ms, sys: 112 ms, total: 320 ms\n",
      "Wall time: 39.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    Finally, here we show how an entire Dataframe containing\n",
    "    several trajectories can be passed to the interpolation\n",
    "    function and how the number of points will be added\n",
    "    to the dataframe based on the user-provided time jump.\n",
    "\"\"\"\n",
    "\n",
    "# Here, Interpolate the original seagulls dataset using random-walk\n",
    "# interpolation and then show the difference in the number of\n",
    "# points to see how the dataframe's trajectories have been\n",
    "# interpolated.\n",
    "\n",
    "rw_ip_gulls = ip.interpolate_position(dataframe=np_gulls,\n",
    "                                      time_jump=3600*4,\n",
    "                                      ip_type='random-walk')\n",
    "print(f\"Original DF length: {len(gulls)}\")\n",
    "print(f\"Random-Walk Interpolated DF length: {len(rw_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF length: 89869\n",
      "Kinematic Interpolated DF length: 157670\n",
      "CPU times: user 142 ms, sys: 99.5 ms, total: 241 ms\n",
      "Wall time: 40.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Here, Interpolate the original seagulls dataset using kinematic\n",
    "# interpolation and then show the difference in the number of\n",
    "# points to see how the dataframe's trajectories have been\n",
    "# interpolated.\n",
    "\n",
    "kin_ip_gulls = ip.interpolate_position(dataframe=np_gulls,\n",
    "                                       time_jump=3600*4,\n",
    "                                       ip_type='kinematic')\n",
    "print(f\"Original DF length: {len(gulls)}\")\n",
    "print(f\"Kinematic Interpolated DF length: {len(kin_ip_gulls)}\")\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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